Water Consumption Pattern Analysis Using Biclustering: When, Why and How
Sensors deployed within water distribution systems collect consumption data that enable the application of data analysis techniques to extract essential information. Time series clustering has been traditionally applied for modeling end-user water consumption profiles to aid water management. Howeve...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-06-01
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| Series: | Water |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4441/14/12/1954 |
| _version_ | 1797481225038856192 |
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| author | Miguel G. Silva Sara C. Madeira Rui Henriques |
| author_facet | Miguel G. Silva Sara C. Madeira Rui Henriques |
| author_sort | Miguel G. Silva |
| collection | DOAJ |
| description | Sensors deployed within water distribution systems collect consumption data that enable the application of data analysis techniques to extract essential information. Time series clustering has been traditionally applied for modeling end-user water consumption profiles to aid water management. However, its effectiveness is limited by the diversity and local nature of consumption patterns. In addition, existing techniques cannot adequately handle changes in household composition, disruptive events (e.g., vacations), and consumption dynamics at different time scales. In this context, biclustering approaches provide a natural alternative to detect groups of end-users with coherent consumption profiles during local time periods while addressing the aforementioned limitations. This work discusses when, why and how to apply biclustering techniques for water consumption data analysis, and further proposes a methodology to this end. To the best of our knowledge, this is the first work introducing biclustering to water consumption data analysis. Results on data from a real-world water distribution system—Quinta do Lago, Portugal—confirm the potentialities of the proposed approach for pattern discovery with guarantees of statistical significance and robustness that entities can rely on for strategic planning. |
| first_indexed | 2024-03-09T22:12:26Z |
| format | Article |
| id | doaj.art-85936a939db04aeb9c56e1debb511c68 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-09T22:12:26Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-85936a939db04aeb9c56e1debb511c682023-11-23T19:30:20ZengMDPI AGWater2073-44412022-06-011412195410.3390/w14121954Water Consumption Pattern Analysis Using Biclustering: When, Why and HowMiguel G. Silva0Sara C. Madeira1Rui Henriques2LASIGE and Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, PortugalLASIGE and Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, PortugalINESC-ID and Instituto Superior Técnico, Universidade de Lisboa, 1000-029 Lisboa, PortugalSensors deployed within water distribution systems collect consumption data that enable the application of data analysis techniques to extract essential information. Time series clustering has been traditionally applied for modeling end-user water consumption profiles to aid water management. However, its effectiveness is limited by the diversity and local nature of consumption patterns. In addition, existing techniques cannot adequately handle changes in household composition, disruptive events (e.g., vacations), and consumption dynamics at different time scales. In this context, biclustering approaches provide a natural alternative to detect groups of end-users with coherent consumption profiles during local time periods while addressing the aforementioned limitations. This work discusses when, why and how to apply biclustering techniques for water consumption data analysis, and further proposes a methodology to this end. To the best of our knowledge, this is the first work introducing biclustering to water consumption data analysis. Results on data from a real-world water distribution system—Quinta do Lago, Portugal—confirm the potentialities of the proposed approach for pattern discovery with guarantees of statistical significance and robustness that entities can rely on for strategic planning.https://www.mdpi.com/2073-4441/14/12/1954water consumption analysisbiclusteringtime seriespattern discoveryclusteringsubspace clustering |
| spellingShingle | Miguel G. Silva Sara C. Madeira Rui Henriques Water Consumption Pattern Analysis Using Biclustering: When, Why and How Water water consumption analysis biclustering time series pattern discovery clustering subspace clustering |
| title | Water Consumption Pattern Analysis Using Biclustering: When, Why and How |
| title_full | Water Consumption Pattern Analysis Using Biclustering: When, Why and How |
| title_fullStr | Water Consumption Pattern Analysis Using Biclustering: When, Why and How |
| title_full_unstemmed | Water Consumption Pattern Analysis Using Biclustering: When, Why and How |
| title_short | Water Consumption Pattern Analysis Using Biclustering: When, Why and How |
| title_sort | water consumption pattern analysis using biclustering when why and how |
| topic | water consumption analysis biclustering time series pattern discovery clustering subspace clustering |
| url | https://www.mdpi.com/2073-4441/14/12/1954 |
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